A framework for uncertainty and risk analysis in Total Maximum Daily Load applications

A New Policy of Water Resources and Environmental Regulation in China

As a developing country, China is facing serious water pollution and scarcity, which indicates the need for integrated water-resource and environmental regulations. Zoning policies have undergone significant advancements to enhance water-resource utilization in China. However, conflicts and overlaps still exist among these policies. To integrate these zoning policies and regulations, the "Three Lines One Permit" (TLOP) water-environment policy was formulated as a new framework, which included the goal for water quality, upper limits on water-resource utilization, and a permit list. This study presents the main achievements of the TLOP as a case-study in Jinan. The territories of Jinan were divided into 158 water-environment control-units (WECUs) and classified into two types of protected zones, three types of pollution-control zones, and ordinary zones. The total maximum pollutant-loads in the 158 WECUs, and 138 townships were calculated. The water-resource-utilization indicator values and ecological demand of key rivers were specified. The permit lists for the water environment at macroscale, mesoscale, and microscale were compiled from four perspectives: spatial constraints, emissions control, risk prevention, and resource utilization. Finally, suggestions were proposed to promote a more scientific and efficient TLOP policy to enhance human-water harmony.

Integrated water-quality management indicators from river to sea: A case study of the Bohai Sea, China

Considering the interrelatedness of river and bay ecosystems, river and bay water quality management is shifting to integrated management across coastlines. Here, an integrated management indicator for the coordinated and efficient nitrogen abatement of the Bohai Sea and its basin was proposed. The terrigenous total nitrogen (TN) allocated load was optimized under the dual water quality constraints for both river and bay using a simulation-optimization method. The contributing jurisdictions were identified by their TN overload rates, and their responsibility apportionment rate for specific nitrogen-polluted segment was quantified. Integrated TN reduction scheme resulted in a 29 % greater reduction in bay and river nitrogen pollution than the equal proportion reduction approach. In 18 % of the watersheds in the Bohai basin, the water quality standards of the river were more restrictive than the standards of the bay. Integrated management scheme has higher coordination of river and sea management objectives.

Stochastic reliability-based risk evaluation and mapping for watershed systems and sustainability (STREAMS)

Mitigating water contamination, improving water security, and increasing sustainability involve environmental awareness and conscientious decision-making by denizens and stakeholders. Achieving such awareness requires visually compelling geospatial decision-making tools that take into account the probabilistic and spatially distributed nature of water contamination. Inspired by the success of weather maps, this paper presents a novel STochastic Reliability-based Risk Evaluation And Mapping for watershed Systems and Sustainability (STREAMS) tool that produces and effectively communicates the risk of water contamination as maps. STREAMS is integrated with ArcGIS geoprocessing tools and uses physics-based reliability theory to compute the spatial distribution of risk, which is defined as the probability of exceeding a safety threshold of water contamination within a watershed. A quantitative analysis of the efficacy of mitigation strategies is conducted by estimating risk reduction from best management practices throughout the entire watershed. Two case studies at different spatial scales are presented, demonstrating STREAMS application to watersheds with varied properties.

A framework for determining the maximum allowable external load that will meet a guarantee probability of achieving water quality targets

Quantifying the maximum external pollutant loading is extremely important for environmental management and ecological restoration. However, huge uncertainty exists in the process of determining accurate external pollutant loads discharging into surface water bodies (e.g., rivers, reservoirs, and bays). In this paper, a comprehensive framework is proposed for determining the maximum allowable external load by combining a dynamic nutrient-balance model with the guarantee probability of achieving a specific water quality target. As an important drinking water source for Beijing, the Miyun Reservoir was chosen as a case study because it is experiencing increasing eutrophication. The main results are as follows. ① The nutrient-balance model has shown a good fit to field observations both in calibration and validation periods using the modified Generalized Likelihood Uncertainty Estimation (GLUE). ② Feasible concentration targets were determined for total phosphorus (TP), total nitrogen (TN), and chlorophyll-a as 0.01 mg/L, 0.76 mg/L, and 4.91 μg/L, respectively. ③ The allowable external load of TP is estimated as 45.10-54.14 t, 23.76-29.58 t, and 8.30-12.78 t for guarantee probabilities of TP control target (e.g., 0.01 mg/L) of 25, 50, and 70%, respectively. While the external TN flux should be reduced by 200.21-480.73 t, 429.33-764.45 t, and 642.40-1069.59 t to meet the TN control target (e.g., 0.76 mg/L) at 25, 50, and 70% guarantee probabilities, respectively, The wide range of allowable external nutrient loading reflects the 95% confidence intervals of the load reduction analysis and indicates the importance of model simulation uncertainty and interpretation of the water quality objective. This paper provides a scientifically sound approach to water quality maintenance for the Miyun Reservoir and other surface water bodies.

Risk-based decision making to evaluate pollutant reduction scenarios

A total maximum daily load (TMDL) is required for water bodies in the U.S. that do not meet applicable water quality standards. Computational watershed models are often used to develop TMDL pollutant reduction scenarios. Uncertainty is inherent in the modeling process. An explicit uncertainty analysis would improve model performance and result in more robust decision making when comparing alternative pollutant reduction scenarios. This paper presents a risk-based framework for evaluating alternative pollutant allocation scenarios considering reliability in achieving water quality goals. We demonstrate a generic routine for the application of Generalized Likelihood Uncertainty Estimation (GLUE) to Hydrological Simulation Program-FORTRAN (HSPF) using existing softwares to evaluate two bacteria reduction scenarios from a recently developed TMDL that addressed a bacterial impairment in a mixed land use watershed in Virginia, U.S. Our probabilistic analysis showed that for reliability levels <25%, the recommended TMDL bacterial load reduction scenario had the same exceedance rate as the full reduction scenario (fully reducing all bacterial loads except wildlife), while for reliability levels between 25% and 50%, the exceedance rates for the two pollutant reduction scenarios were similar, with the TMDL recommended scenario violating the water quality criteria only slightly more often. The full reduction scenario performed better in higher reliability levels, although it could not meet the water quality criteria. Our results indicated that, in this case, achieving water quality goals with very high reliability was not possible, even with extreme levels of pollutant reduction. The risk-based framework presented here illustrates a method to propagate watershed model uncertainty and assess performance of alternative pollutant reduction scenarios using existing tools, thereby enabling decision makers to understand the reliability of a given scenario in achieving water quality goals.

Modeling framework for simulating concentrations of solute chemicals, nanoparticles, and solids in surface waters and sediments: WASP8 Advanced Toxicant Module

Toxicant concentrations in surface waters are of environmental concern due to their potential impacts to humans and wildlife. Numerical models provide system insight, support management decisions, and provide scenario testing on the impacts of toxicants. The Water Quality Analysis Simulation Program (WASP) is a widely used framework for developing site-specific models for simulating toxicant concentrations in surface waters and sediments over a range of complexities and temporal and spatial scales. WASP8, with the Advanced Toxicant module, has been recently released, incorporating a complete architecture redesign for an increased number of state variables and different state variable types. WASP8 incorporates a new structure for simulating light intensity and photoreactions in the water column, including the distinction of 10 different wavelength bands, and nanoparticle heteroaggregation to solids. We present a hypothetical case study, using the Cape Fear River, North Carolina as a representative example for simulating solute chemicals, nanoparticles, and solids to demonstrate the new and updated capabilities of WASP8.

Risk Analysis for Reservoir Real-Time Optimal Operation Using the Scenario Tree-Based Stochastic Optimization Method

The inherent uncertainty of inflow forecasts hinders the reservoir real-time optimal operation. This paper proposes a risk analysis model for reservoir real-time optimal operation using the scenario tree-based stochastic optimization method. We quantify the probability distribution of inflow forecast uncertainty by developing the relationship between two forecast accuracy metrics and the standard deviation of relative forecast error. An inflow scenario tree is generated via Monte Carlo simulation to represent the uncertain inflow forecasts. We establish a scenario tree-based stochastic optimization model to explicitly incorporate inflow forecast uncertainty into the stochastic optimization process. We develop a risk analysis model based on the principle of maximum entropy (POME) to evaluate the uncertainty propagation process from flood forecasts to optimal operation. We apply the proposed methodology to a flood control system in the Daduhe River Basin, China. In addition, numerical experiments are carried out to investigate the effect of two different forecast accuracy metrics and different forecast accuracy levels on reservoir optimal flood control operation as well as risk analysis. The results indicate that the proposed methods can provide decision-makers with valuable risk information for guiding reservoir real-time optimal operation and enable risk-informed decisions to be made with higher reliabilities.

Reliability-Based Water Quality Assessment with Load Resistance Factor Design: Application to TMDL

Effective load reduction strategies rely on an accurate Total Maximum Daily Load (TMDL) calculation, which quantifies contaminant loading from various sources. There is a wide range of methods to consider uncertainties in TMDLs: from simple, conservative assumptions regarding factors that contribute to the TMDL required margin of safety (MOS), to probability-based approaches such as Monte Carlo simulations, which explicitly quantifies TMDL uncertainty. In this paper the authors adapt the Load Resistance Factor Design (LRFD), a rigorous, reliability-based framework, to water quality assessment and the TMDL process. The LFRFD replaces the lumped MOS with design factors that reflect the magnitude and distribution of uncertainty among the various contaminant loads. In addition, it produces load reduction estimates to meet management objectives with a contaminant-specific frequency-based target. The LRFD is computationally efficient and flexible in that, to compute the design factors, the procedure can utilize: measurement data, analytical solutions or model simulation results, as well as full or marginal probability distributions.